Laser doppler vibrometer for remote assessment of structural components

ABSTRACT

A method and system for remotely inspecting the integrity of a structure. This can be performed by a method creating a vibratory response in the structure from a remote location and then measuring the vibratory response of the structure remotely. Alternatively, this can be performed by a system for remotely measuring the integrity of a structure using a vehicle and an artificial neural network, where the vehicle is equipped with a vibratory response device. The vibratory response can be produced by infrasonic and audio frequencies that can be produced by at least a vehicle, motor, or sound recording. The vibratory response can be measured with a laser vibrometer or an audio recording device.

CLAIM OF PRIORITY

This application claims priority to U.S. provisional applicationentitled, “Application of Laser Doppler Vibrometer For Remote Assessmentof Structural Components,” having Ser. No. 60/133,588, filed May 11,1999, now abandoned, which is entirely incorporated herein by reference.

TECHNICAL FIELD

The present invention is generally related to non-destructive evaluationof structures and, more particularly, is related to a system and methodfor remotely measuring the strength integrity of a structure.

BACKGROUND OF THE INVENTION

Electric power transmission lines require regular inspections to ensuresafety and reliability. Hazardous, expensive, and time consuming towerclimbing inspections are typically used to verify the structuralintegrity of pole-tops, cross arms, and other elevated components.“Structural integrity” refers generally to its soundness, or, morespecifically, to the absence of macro- and microstructuralirregularities that are known or suspected to affect the strength of thematerial. In addition to the aforementioned deficiencies, tower climbinginspections are inconsistent and will vary from inspector to inspector.

Structural integrity can be tested by using destructive ornon-destructive techniques. Material testing for quality controlcontinues to be mostly destructive in nature despite efforts to developnon-destructive alternatives that are more feasible in terms of price,convenience and reliability. Although destructive testing is quite oftenmore accurate because the condition of the material is made manifestrather than inferred. The obvious disadvantage is that the material orproduct tested is destroyed or rendered useless by the testing process.Furthermore, testing integrity by removal of already in-placestructures, like cross arms on power lines, is not practical.

Alternatively, structural integrity can be tested using non-destructivetechniques. Most non-destructive testing evaluates the material'scomposition and structure by relying on the interaction of the testedmaterial with sound waves or electromagnetic radiation. Such methodsinvolve monitoring the effect of pressure or electromagnetic wavespassing through the material as they are influenced by flaws orinhomogeneities in the test structure. Monitoring the effects istypically done by making contact between the measuring device and thematerial.

Laser beams are known for use in non-destructive testing to detectstructural defects. For example, a laser beam is projected onto a testobject, the object is vibrated and the pattern of light reflected fromthe object is analyzed. As the frequency and intensity of vibrations arevaried, changes appear in the pattern of light. Particular changesindicate that defects are present in the object. Non-destructivematerials testing systems make use of the relationship between resonantfrequency and the structural soundness of materials.

The analysis in most non-destructive testing of this type relies on therelationship between the material's resonant frequency and the strengthand quality of the material's structure. The resonant frequency of amaterial depends upon, among other things, the material's shape,density, stiffness and the like.

Typically, the tested material structure is vibrated using a known forcethat is in contact with the structure (such as a hammer blow or vibratorexciting a power pole) and the vibrational characteristics of the testedarea is measured. The collected data is used to compute the resonantfrequency of the tested area. Generally, digital computers are used toperform evaluations based on the resonant frequency using knownrelationships. However, this method of creating vibration is timeconsuming and costly.

Acoustic resonance techniques have been used to measure the integrity ofwood. Degradation can be determined by examining the acousticalresonance characteristics of wood. If there is an increase in thedamping of the longitudinal acoustic waves, then the integrity of thewood has been degraded. However, a vibration generator must be attachedto one point on the pole while a sensor is attached at another point onthe pole. Performing this for the hundreds of thousands of transmissionstructures would be an arduous and expensive undertaking.

Another solution was to use the damping loss factor of a material todetermine qualitatively the structural integrity of a material. The dataanalysis was performed using a standard digital analysis technique. Asabove, an electrodynamic shaker is attached to the pole to cause avibration, while the vibration is measured with a laser vibrometer.Using this technique to determine structural integrity for the numeroustransmission structures located in the United States would also bearduous and expensive.

Thus, there is a need to find an apparatus and method to measurestructural integrity safely, remotely, accurately, and in an inexpensivemanner.

SUMMARY OF THE INVENTION

This invention is a method and system for remotely inspecting theintegrity of a structure. One embodiment of this invention is a methodof inspecting the integrity of a structure by creating a vibratoryresponse in the structure from a remote location and then measuring thevibratory response of the structure remotely by an artificial neuralnetwork. The vibratory response can be produced by infrasonic and audiofrequencies that can be produced by at least a vehicle, motor, or soundrecording. The vibratory response can be measured with a laservibrometer or an audio recording device.

A second embodiment of this invention is a method of evaluating theintegrity of a structure by measuring the vibratory response of astructure from a remote location and then evaluating the excitationusing an artificial neural network. The artificial neural network can bea feedforward or self organizing map artificial neural network.

A further embodiment of this invention is a method of remotelyinspecting the integrity of a structure by creating infrasonic and audiofrequencies, which produce vibratory response in the structure. Then thevibratory response is measured and a determination is made by anartificial neural network of whether or not the structure is sound.

Another embodiment of this invention is a system for remotely measuringthe integrity of a structure using a vehicle and an artificial neuralnetwork, where the vehicle is equipped with a vibratory response device.The vehicle can be an aircraft, automobile or any other appropriatevehicle. The vibratory response can be produced by infrasonic and audiofrequencies that can be produced by a vehicle, motor, sound recording orloudspeaker. The vibratory response can be measured with a laservibrometer or an audio recording device. The artificial neural networkcan be a feedforward or self-organizing map artificial neural network.

A final embodiment of this invention is a system for remotely measuringthe integrity of a structure using vehicle and an artificial neuralnetwork, where the vehicle produces an audio frequency.

Other systems, methods, features, and advantages of the presentinvention will be or become apparent to one with skill in the art uponexamination of the following drawings and detailed description. It isintended that all such additional systems, methods, features, andadvantages be included within this description, be within the scope ofthe present invention, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be better understood with reference to the followingdrawings. The components in the drawings are not necessarily to scale,emphasis instead being placed upon clearly illustrating the principlesof the present invention. Moreover, in the drawings, like referencenumerals designate corresponding parts throughout the several views.

FIG. 1 is a schematic of an aircraft acquiring data from a power polecross arm.

FIG. 2A and 2B describe a feedforward artificial neural network, whereFIG. 2A is a block diagram of how information moves through thefeedforward artificial neural network and FIG. 2B describes the topologyof the feedforward artificial neural network.

FIG. 3 is a block diagram of the training procedure of a feedforwardartificial neural network.

FIG. 4 is a block diagram of the procedure for using this invention.

FIG. 5 describes the topology of a self organizing map artificial neuralnetwork.

FIG. 6 is a block diagram of the training procedure of a self organizingmap artificial neural network.

FIG. 7 is a block diagram of the procedure for using this invention.

DETAILED DESCRIPTION

FIG. 1 depicts one embodiment of this invention, where an aircraft isequipped with a laser vibrometer. A preferred embodiment of thisinvention opportunistically uses the vibration produced by the aircraft10 to produce a semi-random, broad-band suite of infrasonic and audiofrequencies 20. The cross arm 30 of the transmission structure 35 isvibratorily excited by the infrasonic and audio frequencies 20. Degradedcross arms vibrate differently than structurally sound cross arms. Thelaser vibrometer 40 emits a laser beam 50 that is aimed at a particularcross bar 30 on a transmission structure 35, while the aircraft 10passes the transmission structure 35. Laser light 60 that is scatteredor reflected by the cross arm 30 is collected by the laser vibrometer 40and saved as response data. The vibratory response and detection areperformed remotely, thus precluding the danger inherent with climbinginspections. The entire process can be performed in less than onesecond. After the vibratory data is collected, an artificial neuralnetwork is used to evaluate the data and distinguish sound cross armsfrom degraded cross arms. Thus, this invention is a method and system ofdetermining structural integrity of structures safely, remotely, and inan economical manner.

The structures that can be inspected include pole-tops, cross arms, andother elevated components on telephone poles, power poles, radio towers,TV towers, cell/mobile phone towers, bridges, structures inmanufacturing supporting vessels, piping, military structures, spacestructures, or other similar types of structures. This invention can beused to inspect structures where there is a need to inspect thestructure from a remote location.

The vibratory response can be produced by a vehicle such as an aircraft,e.g., a helicopter or airplane, or automobile, e.g., a car or truck. Inaddition, the excitation can be produced by a motor (e.g., such as froma lawn mower), sound recording, or any other appropriate vibratoryresponse device. Under some circumstances, it may be viable to useenvironmental noise as the source of the vibratory excitation. Thevibratory response is caused by infrasonic and audio frequencies, orsuite of infrasonic and audio frequencies, produced by a vehicle, motor,sound recording, or other vibratory response device. The infrasonic andaudio frequencies can be produced by the vehicle itself, e.g., motor orpropeller, or by a sound recording. Preferably, the infrasonic and audiofrequencies are produced by the vehicle. Preferably, the infrasonic andaudio frequencies are a semi-random, broad-band suite of audiofrequencies; however, other appropriate infrasonic and audio frequenciescan be used.

Vibrational characteristics are measured by a vibratory responsemeasuring device, preferably a laser vibrometer, but can be measured byany non-contacting device that can measure vibratory response, e.g., aaudio recording device such as a microphone.

In a preferred embodiment, a laser vibrometer is used to measure thevibratory response. In practice, a laser vibrometer operates bytransmitting laser light to the vibrated structure and collecting laserlight scattered or reflected therefrom. The data collected from thevibratory response is termed the vibration data. To increase thereflection of the laser beam a reflective material can be placed ontothe cross arm or other structure.

Measuring vibrational velocity using a laser vibrometer is based on theDoppler principal: measurement of a very slight shift in the wavelengthof laser light when it is scattered or is reflected from a movingobject. Combining the transmitted light with the scattered light causesinterference, where the interference is related to the amount of theshift and thus related to the vibrational velocity of the structure onwhich the laser light is directed.

Preferably, a laser vibrometer is used because laser vibrometers aregenerally more accurate and convenient than other devices for measuringvibrational velocity, but other methods can be used. The laservibrometer is especially convenient in that laser light can betransmitted and collected from a remote location, such as an aircraft ormoving vehicle.

The vibration data collected from the laser vibrometer or alternativedevice can be treated in a number of ways. The following is a preferredembodiment of how vibration data can be preprocessed. First, vibrationdata is collected from the laser vibrometer as Fast Fourier Transform(FFT) data from 0 Hz to 1600 Hz in 4 Hz increments. Data points from 0Hz-792 Hz (199 samples) are put into a data set. There are N data setswhere N is the number of structures measured. Next, the naturallogarithm of each data point is taken and when a data point is zero, dueto instrument sensitivity, that point is made equal to the average ofthe samples on either side of the zero value sample. Then, each data setis normalized by dividing every sample datum point by the maximum datapoint value sampled in that particular spectrum. This normalizes eachcross arm's data from 0 to 1. The purpose for this process is to preventlater analysis to be confounded by signal strength, or vibrationamplitude. Vibration amplitude is not controlled, and is a function ofhow close the noise generator (e.g., helicopter) is to the structure.This information says nothing about structural integrity, and so must benormalized among all the data sets. Normalization is performed for eachof the N data sets or FFT spectrums (each arm). For training data setsthe actual cross arm-breaking force, which corresponds to a particulardata set, and becomes the 200^(th) datum point. The actual breakingstrengths of the cross arms should be normalized from 0 to 1. Next, thedata is put into a 200 point row vector. Further, concatenate every rowvector into one single N by 200 matrix (file). Lastly, save the N by 200matrix in a format that can be presented to the artificial neuralnetwork (ANN). Data sets where the actual breaking strength is not knownonly have 199 points in each data set. Thus, the matrix file will onlybe N by 199.

An ANN will be used to distinguish usable structural members fromnon-usable members in a digital computer simulation of a biologicalcomputing structure. Biological computing is adept at patternrecognition but is a poor method for adding numbers. Any appropriate ANNcan be used to analyze data in this invention including feedforward andself-organizing map ANN's.

Biological computing uses analogical or continuously variable inputvalues. Computed decisions based on these values are weighted sums ofthe inputs. The process is inherently parallel. As a pattern-recognitionengine, the ANN has the advantage of being able to interpolate by makingeducated guess decisions, which are not based on specific priorknowledge. An ANN decision can be based on factors that are unknown,non-linear, or unrecognized. The only requirement is that the neuralnetwork must have had experience with appropriate problems of suchcomplexity, i.e. training.

In general, ANNs can be described as a computing architecture that ismade of parallel interconnections of neural processors. In other words,ANN is a mathematical model patterned after the biological behavior ofneurons, which classify patterns input into the artificial neuralnetwork. In order for an ANN to correctly classify input patterns,adjustable weights and thresholds must be appropriately set for eachneuron or unit of the ANN. The adjusting process of the weights iscommonly referred to as training or learning, which reflects thetraditional iterative nature of biological learning processes.

In general, an ANN includes input neurons, output neurons, and hiddenneurons. A neuron is simply a data processing device capable ofreceiving multiple inputs, processing those inputs, and generating oneor more outputs based on those inputs. Generally, this means that theinput neurons receive a single input, hidden neurons receive severalinputs, and output neurons receive several inputs. The hidden neurons donot receive any input signals from sources outside the ANN. Further,they do not output signals to any devices outside the ANN. Consequently,hidden neurons are hidden from the universe existing outside the ANN.However, ANN's can have feed back loops, where there are two layers ofhidden neurons and the neuron in the later layer is connected to theneuron in the former layer. One skilled in the art would realize thatvariations could be made with the structure of the ANN. Two ANN's thatcan be used in this invention are a feedforward ANN and aself-organizing map ANN.

One embodiment of the ANN is a feedforward ANN (FFANN) as depicted inFIGS. 2A and 2B. Preferably, this FFANN has 199 input neurons (200 inputneurons for training sets), one bias input, and 20 hidden neurons. Thereis only one layer of hidden neurons. The transfer functions aresigmodial nonlinear transfer functions. All input weights are connectedto all the hidden neurons. There is only one output neuron.

FIG. 2A is a block diagram showing how information moves through theFFANN. The data set is input into the input neurons 202 (e.g., IN₁, IN₂,. . . IN₂₀₀). Typically, the first neuron (IN₁,) is programmed to have abias equal to one. This is necessary to ensure proper operation of theFFANN. However, the FFANN will “learn” if the bias is not needed and theweight corresponding to the bias input will be adjusted to zero by theback-propagation algorithm or similar algorithm. The b 2 ^(nd) through200^(th) input neurons (IN₂, . . . IN₂₀₀) will have data points 1through 199 input into them. Then the information is multiplied by theirrespective input weights 204 (e.g., W_(1,1), W_(1,2), . . . W_(1,200) .. . W_(20,1), W_(20,2) . . . W_(20,200)). All 200 input neurons (199data points and 1 bias point) are multiplied by their respective inputweights and are summed at the hidden neurons 206 (HN₁ . . . HN₂₀). Inother words, each hidden neuron receives information from 200 properlyweighted data points. Each hidden neuron produces a summation result,e.g., a₁ . . . a₂₀, that is passed through a nonlinear transfer function(NLF) 208. Then, each NLF 208 produces a result, e.g., d₁ . . . d₂₀,that is multiplied by the output weights 210, e.g., V₁ . . . V₂₀, V₂₁.The bias point is also multiplied by an output weight 210 (V₂₁), butdoes not pass through a NLF 208 as is shown in FIG. 2B. The outputneuron 212 receives the properly weighted values (twenty-one in total)and produces a summation that corresponds to the predicted breakingstrength 214 of a particular cross arm. One skilled in the art wouldunderstand that variation on the number of hidden neurons and layers ofhidden neurons is possible. Additional variations could be envisioned byone skilled in the art.

FIG. 3 generally describes the training stage of the FFANN in thepresent invention. Cross-arm FFT vibration data is gathered by the laservibrometer 302. Selected arms that have been measured by the laservibrometer, a quantity N, are then removed from service and broken, andtheir breaking strengths are measured and stored in 314. The vibrationdata, plus the breaking strength, form a data-set for each of the Ncross arms. These N data-sets are then shuffled into a randomly placedlist of strong and weak arms, called the training set. This is done toprevent the FFANN from trying to learn something about the sequence inwhich the different data-sets are presented to it during training. Thevibration data of each arm is preprocessed 304 according to theaforementioned methods, and the training set is fed into the FFANN 306.Initially, the input and output weights 308 of the FFANN are set torandom values. The FFANN predicts the breaking strength of each data setof the training set 310. The comparator 312 calculates the difference orerror between the predicted and actual known breaking strengths 314.This is performed for each of the data sets in the training set. If theerror is below a particular threshold 316 then the training is complete322. If the error is not below a particular threshold 316 then the datasets in the training set are reshuffled 320 into a new random list. Inaddition, the back-propagation training algorithm 318 uses the error toupdate the input and output weights 308. The aforementioned process isthen repeated, often many thousands of times, until the error is below athreshold value 316, thereby indicating that training of the FFANN iscomplete 322.

The back-propagation training algorithm 318 is set to a learning gainequal to 0.05, learning momentum equal to 0.04, training epochs equal to10,000, linear output, and delta learning rule. Back-propagationtraining algorithms are well known mathematical procedures and oneskilled in the art would understand that the values input into theback-propagation training algorithm are not absolute and other suitablevalues can be used. In general, ANN's are tolerant of imprecision andvarious values can be used to obtain the same result, albeit within anacceptable error limit.

Once the FFANN has been trained, the system is ready to predict thestrength of cross-arms for which breaking strength values are notavailable, FIG. 4. The FFANN is therefore now able to input vibrationaldata for a particular cross-arm and predict its breaking strength.First, the cross-arm vibration data is gathered by the laser vibrometer402. Next, the data is preprocessed 404 according to the methodsoutlined above. Lastly, the data is fed into the ANN 406, which predictsa breaking strength 308 for the particular cross-arm. The details of howthe FFANN operates are discussed above in FIGS. 2A and 2B.

Another embodiment of the ANN for this invention is a self-organizingmap ANN (SOMANN) as shown in FIG. 5. Preferably, this SOMANN has 199input neurons 502 and a 2-dimensional grid or map of output neurons 506.The number of output neurons 506 can vary depending on the resolution orprecision needed. Every input neuron 502 is connected to each outputneuron 506 via connection weights 504. In other words, every outputneuron 506 has 199 connection weights 504 directed towards it from all199 input neurons 502, which is partially depicted in FIG. 5. However,FIG. 5 does not show the input neurons 502 connected to every outputneuron 506 for sake of clarity. One skilled in the art would understandthat variations can be made to the structure or topology of the SOMANNand still accomplish the same goal of this invention.

FIG. 6 generally describes the training process of the SOMANN used inthis invention. Cross-arm FFT vibration data is gathered by the laservibrometer 602 for N cross arms. The breaking strengths of the crossarms are not needed to train the SOMANN, but are used after training todesignate areas of the 2-dimensional map as “strong,” “average,” and“weak,” discussed in more detail below. The vibration data form a dataset for each of the N cross arms. These N data-sets are then shuffledinto a randomly placed list of cross arms, called the training set. Thisis done to prevent the SOMANN from trying to learn something about thesequence in which the different data-sets are presented to it duringtraining. The vibration data of each arm is processed 604 and thetraining set is fed into the SOMANN 606. Initially, the connectingweights 608 of the SOMANN are set to random values. The SOMANN locateseach data set onto the 2-dimensional map 610. The SOMANN analyzes thestatistical properties of the weighted input information and locateseach data set, or cross arm, onto the 2-dimensional map. Moreparticularly, for each output neuron, the input vector, i.e. inputneurons, is multiplied by the corresponding connecting weights toproduce a number called an activation threshold. After this process isperformed for each output neuron, the output neuron with the highestactivation threshold “wins”, i.e. “winner take all” algorithm. Then acomparison is made or error determined between the values of the inputvector and the corresponding connecting weights 616. If the error isbelow a particular threshold then the training of the SOMANN is complete622. However, if the error is not below the particular threshold thenthe data sets are reshuffled into a new random list 620 and resubmittedinto the SOMANN 606 to continue training. In addition, the error is usedto update the connecting weights 218. The aforementioned process is thenrepeated, often many thousands of times, until the error is below athreshold value 616, thereby indicating that training of the SOMANN iscomplete 622.

An alternative to the “winner take all algorithm” is the “Kohonenalgorithm.” This algorithm operates by updating the connecting weightsin some area or neighborhood around the winning output neuron. Oneskilled in the art would understand that alternative algorithms to thewinner take all and Kohonen algorithms can be successfully used in thisinvention.

The SOMANN produces a 2-dimensional map after training, where particularcross arms are put onto various areas of the map. Thus, by determiningthe actual breaking strengths of the cross arms used in the trainingsets, a correlation can be made between the area of the 2-dimensionalmap and the actual breaking strength. Areas of the 2-dimensional map canbe characterized as “strong,” “average,” and “weak.” Other more specificclassifications can be made if necessary. The final result is a2-dimensional map that has areas designated as strong, average, or weak.

Once the SOMANN has been trained, the system is ready to predict thestrength of cross-arms for which breaking strength values are notavailable, FIG. 7. The SOMANN is therefore now able to input vibrationaldata for a particular cross-arm. First, the cross-arm vibration data isgathered by the laser vibrometer 702. Next, the data is processed 704.Lastly, the data is fed into the SOMANN 706, which classifies the crossarm. The classes correspond to a place on the 2-dimensional map obtainedthrough training that correspond to a strong, average, or weak crossarm. For example, the upper right hand portion of the 2-dimensional mapis characterized as strong. Then while analyzing a particular cross arm,the result of the SOMANN is that the cross arm is located in the upperright hand corner of the 2-dimensional map. Thus, the cross arm would beconsidered strong. The details of how the SOMANN operates are discussedabove in FIG. 5.

The FFANN and SOMANN of the present invention can be implemented inhardware, software, firmware, or a combination thereof. In the preferredembodiment(s), the ANN is implemented in software or firmware that isstored in a memory and that is executed by a suitable instructionexecution system. If implemented in hardware, as in an alternativeembodiment, the ANN can be implemented with any or a combination of thefollowing technologies, which are all well known in the art: a discretelogic circuit(s) having logic gates for implementing logic functionsupon data signals, an application specific integrated circuit (ASIC)having appropriate combinational logic gates, a programmable gatearray(s) (PGA), a field programmable gate array (FPGA), etc.

It should be emphasized that the above-described embodiments of thepresent invention, particularly, any “preferred” embodiments, are merelypossible examples of implementations, merely set forth for a clearunderstanding of the principles of the invention. Many variations andmodifications may be made to the above-described embodiment(s) of theinvention without departing substantially from the spirit and principlesof the invention. All such modifications and variations are intended tobe included herein within the scope of this disclosure and the presentinvention and protected by the following claims.

What is claimed is:
 1. A method of inspecting the integrity of astructure comprising: creating a vibratory response in said structureremotely, wherein said vibratory response is measured as vibration data;and measuring the vibratory response remotely, wherein said vibrationdata is preprocessed in a way including: collecting said laservibrometer vibration data as Fast Fourier Transform data in 4 hertzincrements from 0 hertz to 1300 hertz for N data sets, where said N datasets corresponds to the number of said structures measured, and brokenand used for training; dividing the frequency range into 4 hertzincrements from 0 hertz to 792 hertz producing 199 data points for eachdata set; taking the natural logarithm of said 199 data points of eachdata set; normalizing said 199 data points by dividing said 199 datapoints by the largest data point value of that particular data set foreach data set; transforming said 199 data points of each data set into a199 point row vector; concatenating said row vectors into one single Nby 199 matrix; and saving said matrix in a format suitable to present tothe artificial neural network.
 2. The method of claim 1, wherein saidvibratory response is produced by a suite of infrasonic and audiofrequencies.
 3. The method of claim 2, wherein said infrasonic and audiofrequencies are produced by a vehicle.
 4. The method of claim 2, whereinsaid infrasonic and audio frequencies are produced by a motor.
 5. Themethod of claim 2, wherein said infrasonic and audio frequencies areproduced by a sound recording.
 6. The method of claim 1, wherein saidvibratory response is measured with a laser vibrometer.
 7. The method ofclaim 1, wherein said vibratory response is measured with an audiorecording device.
 8. The method of claim 1, wherein said vibration datais evaluated with an artificial neural network.
 9. The method of claim8, wherein said artificial neural network is a feed-forward artificialneural network.
 10. The method of claim 8, wherein said artificialneural network is a self-organizing map artificial neural network. 11.The method of claim 1, wherein said structure comprises a power polecross-arm.
 12. The method of claim 1, wherein the said structure can becoated with a reflecting material.
 13. A method for evaluating theintegrity of a structure comprising: measuring vibratory response insaid structure remotely, wherein said vibratory response is measured asvibration data; and evaluating said excitation with an artificial neuralnetwork, wherein said vibration data is preprocessed in a way including:collecting said laser vibrometer vibration data as Fast FourierTransform data in 4 hertz increments from 0 hertz to 1300 hertz for Ndata sets, where said N data sets corresponds to the number of saidstructures measured, and broken and used for training; dividing thefrequency range into 4 hertz increments from 0 hertz to 792 hertzproducing 199 data points for each data set; taking the naturallogarithm of said 199 data points of each data set; normalizing said 199data points by dividing said 199 data points by the largest data pointvalue of that particular data set for each data set; transforming said199 data points of each data set into a 199 point row vector;concatenating said row vectors into one single N by 199 matrix; andsaving said matrix in a format suitable to present to the artificialneural network.
 14. The method of claim 13, wherein said vibratoryresponse is measured with a laser vibrometer.
 15. The method of claim13, wherein said vibratory response is measured with an audio recordingdevice.
 16. The method of claim 13, wherein said artificial neuralnetwork is a feed-forward artificial neural network.
 17. The method ofclaim 13, wherein said artificial neural network is a self-organizingmap.
 18. The method of claim 13, wherein said structure comprises apower pole cross-arm.
 19. The method of claim 13, wherein the saidstructure can be coated with a reflecting material.
 20. A method ofremotely inspecting the integrity of a structure comprising: creatinginfrasonic and audio frequencies; producing a vibratory response in saidstructure using said frequencies, wherein said vibratory response ismeasured as vibration data; measuring said vibratory excitation; anddetermining said structural integrity using an artificial neuralnetwork, wherein said vibration data is preprocessed in a way including:collecting said laser vibrometer vibration data as Fast FourierTransform data in 4 hertz increments from 0 hertz to 1300 hertz for Ndata sets, where said N data sets corresponds to the number of saidstructures measured; dividing the frequency range into 4 hertzincrements from 0 hertz to 792 hertz producing 199 data points for eachdata set; taking the natural logarithm of said 199 data points of eachdata set; normalizing said 199 data points by dividing said 199 datapoints by the largest data point value of that particular data set foreach data set; transforming said 199 data points of each data set into a199 point row vector; concatenating said row vectors into one single Nby 199 matrix; and saving said matrix in a format suitable to present tothe artificial neural network.
 21. The method of claim 20, wherein saidinfrasonic and audio frequencies are a semirandom, broad-band suite ofaudio frequencies.
 22. The method of claim 20, wherein creatinginfrasonic and audio frequencies comprises: creating infrasonic andaudio frequencies with a vehicle.
 23. The method of claim 20, whereincreating infrasonic and audio frequencies comprises: creating infrasonicand audio frequencies with a motor.
 24. The method of claim 20, whereincreating infrasonic and audio frequencies comprises: creating infrasonicand audio frequencies with playing a sound recording of infrasonic andaudio frequencies.
 25. The method of claim 20, wherein said vibratoryresponse is measured with a laser vibrometer.
 26. The method of claim20, wherein said vibratory response is measured with an audio recordingdevice.
 27. The method of claim 20, wherein said artificial neuralnetwork is a feed-forward artificial neural network.
 28. The method ofclaim 20, wherein said artificial neural network is a self-organizingmap artificial neural network.
 29. The method of claim 20, wherein saidstructure comprises a power pole cross-arm.
 30. The method of claim 20,wherein the said structure can be coated with a reflecting material. 31.A system for remotely measuring the integrity of a structure comprising:a vehicle, wherein said vehicle comprises a vibratory response measuringdevice; and a neural network.
 32. The system of claim 31, wherein saidvehicle comprises an aircraft.
 33. The system of claim 31, wherein saidvehicle comprises an automobile.
 34. The system of claim 31, whereinsaid structure is vibratorily excited by an audio frequency.
 35. Thesystem of claim 31, wherein said audio frequency is produced by saidvehicle.
 36. The system of claim 31, wherein said audio frequency isproduced by a motor.
 37. The system of claim 31, wherein said audiofrequency is produced from a sound recording.
 38. The system of claim31, wherein said infrasonic and audio frequency comprises a semi-random,broad-band suite of audio frequencies.
 39. The system of claim 31,wherein said vibratory measuring device is a laser vibrometer.
 40. Thesystem of claim 31, wherein said vibratory measuring device is an audiorecording device.
 41. The system of claim 31, wherein said vibratoryresponse is measured as vibration data.
 42. The system of claim 41,wherein said vibration data is preprocessed in a way comprising:collecting said laser vibrometer vibration data as Fast FourierTransform data in 4 hertz increments from 0 hertz to 1300 hertz for Ndata sets, where said N data sets corresponds to the number of saidstructures measured, and broken and used for training; dividing thefrequency range into 4 hertz increments from 0 hertz to 792 hertzproducing 199 data points for each data set; taking the naturallogarithm of said 199 data points of each data set; normalizing said 199data points by dividing said 199 data points by the largest data pointvalue of that particular data set for each data set; transforming said199 data points of each data set into a 199 point row vector;concatenating said row vectors into one single N by 199 matrix; andsaving said matrix in a format suitable to present to the artificialnetwork.
 43. The system of claim 42, wherein said data set comprises 200data points, where the 200^(th) data point is the actual breakingstrength of said structure.
 44. The system of claim 31, wherein saidartificial neural network is a feed-forward artificial neural network.45. The system of claim 31, wherein said artificial neural network is aself-organizing map artificial neural network.
 46. A system for remotelymeasuring the integrity of a structure comprising: a vehicle, whereinsaid vehicle produces an audio frequency that causes a vibratoryresponse in said structure and wherein said vehicle comprises avibratory response measuring device; and a neural network, wherein saidneural network evaluates said vibratory excitation.
 47. The system ofclaim 46, wherein said vehicle comprises an aircraft.
 48. The system ofclaim 46, wherein said vehicle comprises an automobile.
 49. The systemof claim 46, wherein said audio frequency comprises a semi-random,broad-band suite of audio frequencies.
 50. The system of claim 46,wherein said vibratory measuring device is a laser vibrometer.
 51. Thesystem of claim 46, wherein said vibratory measuring device is an audiorecording device.
 52. The system of claim 46, wherein said vibratoryresponse is measured as vibration data.
 53. The method of claim 52,wherein said vibration data is preprocessed in a way comprising:collecting said laser vibrometer vibration data as Fast FourierTransform data in 4 hertz increments from 0 hertz to 1300 hertz for Ndata sets, where said N data sets corresponds to the number of saidstructures measured, and broken and used for training; dividing thefrequency range into 4 hertz increments from 0 hertz to 792 hertzproducing 199 data points for each data set; taking the naturallogarithm of said 199 data points of each data set; normalizing said 199data points by dividing said 199 data points by the largest data pointvalue of that particular data set for each data set; transforming said199 data points of each data set into a 199 point row vector;concatenating said row vectors into one single N by 199 matrix; andsaving said matrix in a format suitable to present to the artificialneural network.
 54. The method of claim 53, wherein said data setcomprises 200 data points, where the 200^(th) data point is the actualbreaking strength of said structure.
 55. The system of claim 53, whereinsaid artificial neural network is a feed-forward artificial neuralnetwork.
 56. The system of claim 53, wherein said artificial neuralnetwork is a self-organizing map artificial neural network.
 57. A methodof inspecting the integrity of a structure comprising: creating avibratory response in said structure remotely, wherein said vibratoryresponse is measured as vibration data including 200 data points, wherethe 200^(th) data point is the actual breaking strength of saidstructure; and measuring the vibratory response remotely.
 58. The methodof claim 57, wherein said vibratory response is produced by a suite ofinfrasonic and audio frequencies.
 59. The method of claim 58, whereinsaid infrasonic and audio frequencies are produced by a vehicle.
 60. Themethod of claim 58, wherein said infrasonic and audio frequencies areproduced by a motor.
 61. The method of claim 58, wherein said infrasonicand audio frequencies are produced by a sound recording.
 62. The methodof claim 57, wherein said vibratory response is measured with a laservibrometer.
 63. The method of claim 57, wherein said vibratory responseis measured with an audio recording device.
 64. A method for evaluatingthe integrity of a structure comprising: measuring vibratory response insaid structure remotely, wherein said vibratory response is measured asvibration data including 200 data points, where the 200^(th) data pointis the actual breaking strength of said structure; and evaluating saidexcitation with an artificial neural network.
 65. The method of claim64, wherein said artificial neural network is a feed-forward artificialneural network.
 66. The method of claim 64, wherein said artificialneural network is a self-organizing map.
 67. The method of claim 64,wherein said structure comprises a power pole cross-arm.
 68. The methodof claim 64, wherein the said structure can be coated with a reflectingmaterial.
 69. A method of remotely inspecting the integrity of astructure comprising: creating infrasonic and audio frequencies;producing a vibratory response in said structure using said frequencies,wherein said vibratory response is measured as vibration data including200 data points, where the 200^(th) data point is the actual breakingstrength of said structure; measuring said vibratory excitation; anddetermining said structural integrity using an artificial neuralnetwork.
 70. The method of claim 69, wherein said infrasonic and audiofrequencies are a semi-random, broad-band suite of audio frequencies.71. The method of claim 69, wherein creating infrasonic and audiofrequencies comprises: creating infrasonic and audio frequencies with avehicle.
 72. The method of claim 69, wherein creating infrasonic andaudio frequencies comprises: creating infrasonic and audio frequencieswith a motor.
 73. The method of claim 69, wherein creating infrasonicand audio frequencies comprises: creating infrasonic and audiofrequencies with playing a sound recording of infrasonic and audiofrequencies.
 74. The method of claim 69, wherein said vibratory responseis measured with a laser vibrometer.
 75. The method of claim 69, whereinsaid vibratory response is measured with an audio recording device.